laokpa Goto Github PK

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laokpa's Projects

stock_market_reinforcement_learning

This project provides a stock market environment using OpenGym with Deep Q-learning and Policy Gradient.

stock_play

Looking at patterns in stock price movements using pricing and volume data

stock_prediction

Predicting Stock Price by Time Series Analysis and generating signal to Buy or Sell by Genetic Algorithm (AI)

stock_price_movement_prediction_rnn_cnn_ffnn

The random forest, FFNN, CNN and RNN models are developed to predict the movement of future trading price of Netflix (NFLX) stock using transaction data from the Limit Order Book (LOB).

stockmarket-data-analysis-vizualization

Using Python to extract, perform data analysis & visualizations, and derive insights from the historical day-to-day price-volume data of various stocks registered on National Stock Exchange(NSE) of India.

stockmarketpredictionusingneat

Generating an ANN to predict stock market using NEAT.

stockpredictionai

In this noteboook I will create a complete process for predicting stock price movements. Follow along and we will achieve some pretty good results. For that purpose we will use a Generative Adversarial Network (GAN) with LSTM, a type of Recurrent Neural Network, as generator, and a Convolutional Neural Network, CNN, as a discriminator. We use LSTM for the obvious reason that we are trying to predict time series data. Why we use GAN and specifically CNN as a discriminator? That is a good question: there are special sections on that later.

stockpredictionrnn

High Frequency Trading Price Prediction using LSTM Recursive Neural Networks

stocks-analysis-dashboard

WIP - Dashboard with stock price analysis (Volume, RSI, MACD)

stocks-transaction-calculation

Created a prediction model to get Profit Percentage using Regression and Win Trade using Classification. Selected top features like 'EntrySeconds', 'Intraday Current Market Gap Percentage', 'All Exchanges Volume' from US stock market trades data using Random Forest

structured_data_random_features_for_large-scale_kernel_machines

Kernel machines such as the Support Vector Machine are widely used in solving machine learning problem, since they can approximate any function or decision boundary arbitrary well with enough training data. However, those methods applied on the kernel matrix (Gram matrix) of the data scale poorly with the size of the training dataset. The kernel trick may become intractable to compute as the computation and storage requirements for the kernel trick are exponentially proportional to the number of samples in the dataset. It takes a long time to train a model when training examples have big volume. For some specialized algorithms for linear Support Vector Machines, they operate much more quickly when the dimensionality of data is small because they operate on the covariance matrix rather than the kernel matrix of the training data. This paper we’ve chosen proposes a way to combine the advantages of the linear and nonlinear approaches. This method transformed the training and evaluation of any kernel machine by mapping the input data to a randomized low-dimensional feature space in order to create corresponding opera- tions of a linear machine. Those randomized features are designed to ensure that the inner products of the transformed data are nearly equal to those in the feature space of a user specific shift-invariant kernel. This method gives competitive results with state-of-the-art kernel-based classification and re- gression algorithms. What’s more, random features fix the problem of large scale of training data when computing the kernel matrix. The results have similar or even better testing error.